{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "rcParams['font.size'] = 14\n",
    "\n",
    "# This is optional, but it allows zooming and panning of figures\n",
    "# Uncomment the block below if you want this feature\n",
    "\n",
    "interactive = False # Set this to false if no iteractive data are desired.\n",
    "if interactive:\n",
    "    try:\n",
    "        import mpld3\n",
    "        from mpld3 import plugins\n",
    "        mpld3.enable_notebook()\n",
    "        print('Interactive plotting enabled')\n",
    "    except:\n",
    "        interactive = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReachPreProcessor Example\n",
    "\n",
    "In this example, we demonstrate how to load a set of predefined reaches and process them so that they are better suited to the data and applications.\n",
    "\n",
    "In this case, the reaches are from the North America Global River Width Database for Large Rivers. The data were made by [Tamlin Pavelsky](mailto:pavelsky@unc.edu) and [George Allen](mailto:georgehenryallen@gmail.com) and modified by [Ernesto Rodriguez](mailto:ernesto.rodriguez@jpl.nasa.gov) so that reaches appeared as topologically connected. The results are in the shapefile `nAmerica_GRWDL_river_topo.shp`. A rapidly searchable width database, segemented by reach has been made into a pytables hdf5 file and will be used as width data base.\n",
    "\n",
    "As sample data for this data collection, we use some simulated SWOT data over the Sacrramento River."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../../../RiverObsTestData'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from os.path import exists, join\n",
    "def find_riverobs_test_data_dir():\n",
    "    \"\"\"Fin the location of the test data root directory\"\"\"\n",
    "    \n",
    "    if 'RIVEROBS_TESTDATA_DIR' in os.environ:\n",
    "        test_data_dir = os.environ('RIVEROBS_TESTDATA_DIR')\n",
    "    else: # try the default location\n",
    "        test_data_dir = '../../../RiverObsTestData'\n",
    "        \n",
    "    if not exists(test_data_dir):\n",
    "        print('You must either set the environment variable RIVEROBS_TESTDATA_DIR')\n",
    "        print('or locate the test data directory at ../../../RiverObsTestData')\n",
    "        raise Exception('Test data directory not found.')\n",
    "        \n",
    "    return test_data_dir\n",
    "\n",
    "data_dir = find_riverobs_test_data_dir()\n",
    "data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the file for the width data base\n",
    "\n",
    "db_dir = join(data_dir,'GRWDL')\n",
    "width_db_file = join(db_dir,'nAmerica_GRWDL.h5')\n",
    "\n",
    "# This is the SWOT data\n",
    "\n",
    "l2_file = join(data_dir,'L2','L2v0','simulated_sacramento_swot_test_data_v0.nc')\n",
    "assert exists(l2_file)\n",
    "\n",
    "# This is the file for the reach data base\n",
    "\n",
    "shape_file_root = join(db_dir,'nAmerica_GRWDL_river_topo','nAmerica_GRWDL_river_topo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the Required Modules and Initialize Objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erodrigu/anaconda/envs/RiverObs/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "from SWOTRiver import SWOTL2\n",
    "from RiverObs import ReachPreProcessor, RiverReachWriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the SWOT simulated data to find the data bounding box and define the projection function. For the editing function, use the true data classification and true water location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cant Find range_index, or azimuth_index variables, assuming 2D-image image coordinates (like from a gdem)\n",
      "WARNING: Input file does not contain range/azimuth index. Functions relying on radar coordinates WILL break!\n"
     ]
    }
   ],
   "source": [
    "class_list=[1]\n",
    "lat_kwd='no_layover_latitude'\n",
    "lon_kwd='no_layover_longitude'\n",
    "\n",
    "l2 = SWOTL2(l2_file,class_list=class_list,lat_kwd=lat_kwd,lon_kwd=lon_kwd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the pre-processor with the GRWDL data. The next step scans through the database and finds all of the overlapping reaches in the data base (only one is found). A small buffer of about 2km is placed about the data bounding box to make sure the river is not cut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_buffer = 0.02\n",
    "reaches = ReachPreProcessor(shape_file_root, l2,clip_buffer=clip_buffer,width_db_file=width_db_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reaches found: 1\n",
      "Reach indexes: [704]\n",
      "Reach 0 Metadata\n",
      "{'latmax': 40.03200149536133, 'break_idx': 1388940, 'width_mean': 115.74655151367188, 'reach': 375745.03125, 'reach_idx': 704, 'width_max': 532.0, 'width_std': 52.802425384521484, 'width_min': 30.0, 'lonmin': -122.11900329589844, 'npoints': 10144, 'lonmax': -121.5009994506836, 'latmin': 38.16350173950195}\n"
     ]
    }
   ],
   "source": [
    "print('Number of reaches found:',reaches.nreaches)\n",
    "print('Reach indexes:',reaches.reach_idx)\n",
    "for i,reach in enumerate(reaches):\n",
    "    print('Reach %d Metadata'%i)\n",
    "    print(reach.metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a guide to editing the reaches, plot the data and reach locations for each reach found. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UWlcppTYDW7XWX+3g9c8A6Vrra8LSFgLvALla6yOdnT8RAwOYeZbuf2UX3s4W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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134d1e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6,6)\n",
    "plot(l2.lon[::10],l2.lat[::10],'o',alpha=0.1,color='aqua')\n",
    "for reach in reaches:\n",
    "    plot(reach.lon,reach.lat,'.',alpha=0.4,label='Reach %d'%i)\n",
    "legend(loc='best')\n",
    "title('Reaches vs Data')\n",
    "if interactive:\n",
    "    plugins.connect(gcf(),plugins.MousePosition(fmt='.3f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 1**: Clearly the reach is much longer than the bit of data imaged. We can cut it down in several way, as demonstrated in the following."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break reach by predefined points\n",
    "\n",
    "Use interactive graphics in the iPython notebook to pick the location of the breaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_lons = [-122.010,-121.965,-121.962,-121.978]\n",
    "start_lats = [39.760,39.735,39.685,39.648]\n",
    "end_lons = [-121.965,-121.962,-121.978,-121.998]\n",
    "end_lats = [39.735,39.685,39.648,39.601]\n",
    "\n",
    "reach_start_list = list(zip(start_lons,start_lats))\n",
    "reach_end_list = list(zip(end_lons,end_lats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jDrZ1/mgMDGDnWXrg5e142lq0YUwq6ROGcff0kXwuK4X4Xs2hUmow62hg6OoA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\nZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134d6a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6,6)\n",
    "plot(l2.lon[::10],l2.lat[::10],'o',alpha=0.1,color='aqua')\n",
    "for reach in reaches:\n",
    "    plot(reach.lon,reach.lat,'.',alpha=0.4,label='Reach %d'%i)\n",
    "scatter(start_lons,start_lats,s=300,c='r',marker='+')\n",
    "scatter(end_lons,end_lats,s=300,c='g',marker='x')\n",
    "legend(loc='best')\n",
    "title('Reaches vs Data')\n",
    "if interactive:\n",
    "    plugins.connect(gcf(),plugins.MousePosition(fmt='.3f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "edited_reaches = reaches.split_by_coordinates(reach_start_list,reach_end_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('reach_idx', 0), ('lonmin', -122.01000213623051), ('lonmax', -121.96499633789061), ('latmin', 39.731700890249243), ('latmax', 39.759498589237268), ('reach', 6639.4358592689023), ('npoints', 142), ('break_idx', 141), ('preach_idx', 0), ('pstart_s', 27671.332707718801), ('pend_s', 34310.768566987703), ('pidxstart', 641), ('pidxend', 782), ('width_mean', 152.21830985915494), ('width_max', 258), ('width_min', 60), ('width_std', 44.661814706951525)])\n",
      "OrderedDict([('reach_idx', 1), ('lonmin', -121.96499633789061), ('lonmax', -121.94200134277338), ('latmin', 39.685001366300874), ('latmax', 39.736198418327533), ('reach', 9459.6523086880406), ('npoints', 236), ('break_idx', 377), ('preach_idx', 0), ('pstart_s', 34310.768566987703), ('pend_s', 43770.420875675743), ('pidxstart', 782), ('pidxend', 1017), ('width_mean', 133.30084745762713), ('width_max', 379), ('width_min', 42), ('width_std', 52.415373271722665)])\n",
      "OrderedDict([('reach_idx', 2), ('lonmin', -121.99900054931641), ('lonmax', -121.9599990844727), ('latmin', 39.646499626780212), ('latmax', 39.685001366300874), ('reach', 13002.055684498911), ('npoints', 296), ('break_idx', 673), ('preach_idx', 0), ('pstart_s', 43770.420875675743), ('pend_s', 56772.476560174655), ('pidxstart', 1017), ('pidxend', 1312), ('width_mean', 142.07432432432432), ('width_max', 381), ('width_min', 60), ('width_std', 56.51172937520046)])\n",
      "OrderedDict([('reach_idx', 3), ('lonmin', -121.9970016479492), ('lonmax', -121.9769973754883), ('latmin', 39.60100173247104), ('latmax', 39.64899825349039), ('reach', 8927.8773475175767), ('npoints', 217), ('break_idx', 890), ('preach_idx', 0), ('pstart_s', 56772.476560174655), ('pend_s', 65700.353907692232), ('pidxstart', 1312), ('pidxend', 1528), ('width_mean', 136.76036866359448), ('width_max', 270), ('width_min', 60), ('width_std', 36.239445574295004)])\n"
     ]
    }
   ],
   "source": [
    "for reach in edited_reaches:\n",
    "    print(reach.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h/RpKpwENJKVUXPx0w09Zun4p1513HbWmlvq2eoYkDWHhmIWsPrSa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136f4f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6,6)\n",
    "for i,reach in enumerate(edited_reaches):\n",
    "    plot(reach.lon,reach.lat,'.',alpha=0.4,label='Reach %d'%i)\n",
    "scatter(start_lons,start_lats,s=300,c='r',marker='+')\n",
    "scatter(end_lons,end_lats,s=300,c='g',marker='x')\n",
    "legend(loc='best')\n",
    "title('Edited Reaches')\n",
    "if interactive:\n",
    "    plugins.connect(gcf(),plugins.MousePosition(fmt='.3f'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break reach by constant reach length\n",
    "\n",
    "One possible way to break the reaches, which is not particularly sensible from a hydrologic perspective, is to break the reaches into sections of equal lengtgh. This process is demonstarted below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_s = 25.e3\n",
    "ds = 10.e3\n",
    "end_s = start_s + 4*ds\n",
    "edited_reaches = reaches.split_by_reach_length(ds,start_s=start_s,end_s=end_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('reach_idx', 0), ('lonmin', -122.02899932861331), ('lonmax', -121.95899963378912), ('latmin', 39.731700890249243), ('latmax', 39.770999901498683), ('reach', 9935.3353558792951), ('npoints', 217), ('break_idx', 216), ('preach_idx', 0), ('pstart_s', 25054.055977183009), ('pend_s', 34989.391333062304), ('pidxstart', 582), ('pidxend', 798), ('width_mean', 143.09216589861751), ('width_max', 258), ('width_min', 60)])\n",
      "OrderedDict([('reach_idx', 1), ('lonmin', -121.96299743652341), ('lonmax', -121.94200134277338), ('latmin', 39.676399223962711), ('latmax', 39.735298149772468), ('reach', 9889.7441916725729), ('npoints', 251), ('break_idx', 467), ('preach_idx', 0), ('pstart_s', 35077.958664585014), ('pend_s', 44967.702856257587), ('pidxstart', 799), ('pidxend', 1049), ('width_mean', 140.17529880478088), ('width_max', 381), ('width_min', 42)])\n",
      "OrderedDict([('reach_idx', 2), ('lonmin', -121.99900054931641), ('lonmax', -121.9599990844727), ('latmin', 39.652400963452969), ('latmax', 39.676601402917861), ('reach', 9980.9551878854472), ('npoints', 223), ('break_idx', 690), ('preach_idx', 0), ('pstart_s', 45000.73901314364), ('pend_s', 54981.694201029088), ('pidxstart', 1050), ('pidxend', 1272), ('width_mean', 139.49327354260089), ('width_max', 296), ('width_min', 60)])\n",
      "OrderedDict([('reach_idx', 3), ('lonmin', -121.99400329589839), ('lonmax', -121.96800231933589), ('latmin', 39.605899803762526), ('latmax', 39.652099602368907), ('reach', 9964.8309212005042), ('npoints', 238), ('break_idx', 928), ('preach_idx', 0), ('pstart_s', 55015.153769774319), ('pend_s', 64979.984690974823), ('pidxstart', 1273), ('pidxend', 1510), ('width_mean', 134.83193277310923), ('width_max', 270), ('width_min', 60)])\n"
     ]
    }
   ],
   "source": [
    "for reach in edited_reaches:\n",
    "    print(reach.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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c7m0U3FthHztVGNYS6t8lgxVbyl/tYPL1fejRNj2stWXbaxtTd1jAqQQLON+eOxj1aJvO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\n2LaNgzNfd+qSnEzbXzxO8eEjMXXTlRW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wPNB5av0Gij/6CPV5mLXi66+9EwoSr+5HYnp6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\ngGOMMSYqLOAYY4yJCgs4xhhjouL/AN4rYVVpuDx9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136f616a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(6,6)\n",
    "for i,reach in enumerate(edited_reaches):\n",
    "    plot(reach.lon,reach.lat,'.',alpha=1,label='Reach %d'%i)\n",
    "legend(loc='best')\n",
    "title('Edited Reaches')\n",
    "if interactive:\n",
    "    plugins.connect(gcf(),plugins.MousePosition(fmt='.3f'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output the edited reaches and width data base\n",
    "\n",
    "Once the reaches are edited, the results can be written to a new OGR supported data format. The default format is \"ESRI Shapefile\", which is the format expected by the ReachExtractor class. Also, if desired, output the width data base in hdf5 pytables format.\n",
    "\n",
    "First, initialize the RiverReachWriter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_output_name = 'edited_nodes'\n",
    "reach_output_name = 'edited_reaches'\n",
    "\n",
    "\n",
    "!rm -rf $node_output_name\n",
    "!rm -rf $reach_output_name\n",
    "\n",
    "node_output_variables = ['width']\n",
    "reach_output_variables = edited_reaches[0].metadata.keys()\n",
    "reach_writer = RiverReachWriter(edited_reaches,node_output_variables,reach_output_variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, write as node shape files as Point data with dbf fields for each point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "reach_writer.write_nodes_ogr(node_output_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the reach data as LineString shapefiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "reach_writer.write_reaches_ogr(reach_output_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to show that it can be done, and for visualization, write them also as KML:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edited_nodes.kml   edited_reaches.kml\r\n"
     ]
    }
   ],
   "source": [
    "reach_writer.write_nodes_ogr(node_output_name+'.kml',driver='KML')\n",
    "reach_writer.write_reaches_ogr(reach_output_name+'.kml',driver='KML')\n",
    "!ls *.kml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, one can write a new width data base, if one was provided to begin width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edited_width_db.h5\r\n"
     ]
    }
   ],
   "source": [
    "width_db_file = 'edited_width_db'\n",
    "river_df, reach_df = reach_writer.write_width_db(width_db_file,output_format='h5')\n",
    "!ls *.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   break_idx     latmax     latmin      lonmax      lonmin  npoints  \\\n",
      "0        216  39.771000  39.731701 -121.959000 -122.028999      217   \n",
      "1        467  39.735298  39.676399 -121.942001 -121.962997      251   \n",
      "2        690  39.676601  39.652401 -121.959999 -121.999001      223   \n",
      "3        928  39.652100  39.605900 -121.968002 -121.994003      238   \n",
      "\n",
      "         reach  width_max  width_mean  width_min  width_std  \n",
      "0  9964.823242        258  143.092166         60  43.281426  \n",
      "1  9964.823242        381  140.175299         42  60.407259  \n",
      "2  9964.823242        296  139.493274         60  51.019461  \n",
      "3  9964.823242        270  134.831933         60  37.838516  \n"
     ]
    }
   ],
   "source": [
    "print(reach_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   width  nchannels  reservoir        long        lat  reach_index       reach\n",
      "0    108          1          0 -122.028999  39.771000            0   22.024439\n",
      "1     67          1          0 -122.028999  39.770802            0   55.484642\n",
      "2     67          1          0 -122.028999  39.770500            0   88.944847\n",
      "3    127          1          0 -122.028999  39.770199            0  121.981506\n",
      "4     84          1          0 -122.028999  39.769901            0  210.507629\n"
     ]
    }
   ],
   "source": [
    "print(river_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     width  nchannels  reservoir        long        lat  reach_index  \\\n",
      "924     84          1          0 -121.992996  39.606899            3   \n",
      "925     84          1          0 -121.992996  39.606701            3   \n",
      "926    108          1          0 -121.994003  39.606400            3   \n",
      "927    108          1          0 -121.994003  39.606098            3   \n",
      "928     67          1          0 -121.994003  39.605900            3   \n",
      "\n",
      "           reach  \n",
      "924  9816.604492  \n",
      "925  9909.340820  \n",
      "926  9942.799805  \n",
      "927  9964.823242  \n",
      "928  9964.823242  \n"
     ]
    }
   ],
   "source": [
    "print(river_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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